Water and wetland are components of natural resources that play an indispensable role in the ecological environment and water circulation within drainage basins. Using GFDM-1 satellite images with a spatial resolution higher than 0.5m, we performed a water and wetland monitoring application research in Ganzhou, located in Gansu Province in northwestern China. The findings indicate that with high resolution and nine spectral bands, GFDM-1 images provide significant advantages in achieving high quality fine classification. Specifically, it achieved a total classification accuracy of 92.38% for water and 86.19% for wetlands within the demonstration area. In general, the GFDM-1 satellite remote sensing images have good classification and target recognition capabilities, demonstrating significant potential for application in water and wetland monitoring.

As an important part of the renewable energy, photovoltaic power generation industry has developed rapidly all around China in recent years, however some land use problems have also emerged. Therefore it is of great significance to monitor the number and distribution of photovoltaic power stations timely and accurately with high-resolution satellite images for the healthy development of photovoltaic industry. Combined with the improved DeepLab V3+ model and the ResNeSt-50 backbone network, the paper designs an effective photovoltaics extraction semantic segmentation algorithm and trains the new extraction model iteratively by making full use of big and various photovoltaic land samples. Photovoltaics are extracted accurately all over China with Chinese high-resolution satellite images, following a series of post-processing algorithms, such as binarizing, small and pseudo targets automatic removing, etc. Results show that the accuracy rate of photovoltaic land extraction is about 72.36% and the recall rate is about 91.06%. This precision is good enough for photovoltaic land extraction nationwide annually and the proposed deep learning model is efficient, small and can widely be used with other natural resources target extraction.

Abstract Motivated by $g$-extra connectivity, the $g$-extra diagnosability is proposed as a better and more realistic measurement for fault diagnosis of interconnection networks, which is defined as the maximum number of faulty vertices that can be identified when each remaining component has no fewer $g+1$ vertices. Under the MM* model, a variety of interconnection networks’ $g$-extra diagnosability have been investigated, such as hypercube, folded hypercube, $(n,k)$-star network, alternating group graph, etc. These results mostly share similar derivation processes to derive the $g$-extra diagnosability of involved networks by using the $g$-extra connectivity. Therefore, a general approach to derive the $g$-extra diagnosability of a network from its $g$-extra connectivity was investigated in (Wang, S. Y. and Wang, M. (2019) The $g$-good-neighbor and $g$-extra diagnosability of networks. Theor. Comput. Sci., 773, 107–114) and (Huang, Y. Z., Lin, L. M. and Xu, L. (2020) A new proof for exact relationship between extra connectivity and extra diagnosability of regular connected graphs under MM* model. Theor. Comput. Sci., 828–829, 70–80). However, there are some shortcomings in both references. By summarizing the existing shared practices, we propose a new relationship between the $g$-extra connectivity and the $g$-extra diagnosability of networks under the MM* model. As applications, we derive the $g$-extra diagnosability of bijective connection networks and $(n,k)$-star graphs.

The diagnosability of a system is defined as the maximum number of faulty processors that the system can guarantee to identify, which plays an important role in measuring of the reliability of multiprocessor systems. In the work of Peng et al. in 2012, they proposed a new measure for fault diagnosis of systems, namely,$g$-good-neighbor conditional diagnosability. It is defined as the diagnosability of a multiprocessor system under the assumption that every fault-free node contains at least$g$fault-free neighbors, which can measure the reliability of interconnection networks in heterogeneous environments more accurately than traditional diagnosability. The$k$-ary$n$-cube is a family of popular networks. In this study, we first investigate and determine the$R_g$-connectivity of$k$-ary$n$-cube for$0\le g\le n.$Based on this, we determine the$g$-good-neighbor conditional diagnosability of$k$-ary$n$-cube under the PMC model and MM* model for$k\ge 4, n\ge 3$and$0\le g\le n.$Our study shows the$g$-good-neighbor conditional diagnosability of$k$-ary$n$-cube is several times larger than the classical diagnosability of$k$-ary$n$-cube.

Most paddy rice in southern China grows in warm, humid and rainy areas where it is hard to acquire optical remote sensing data. In this study, a semi-empirical backscattering model was proposed to estimate leaf area index (LAI) of rice in the area using ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization data. Ground measurements of LAI, water content and height of rice in the test site were collected and the model fitted at the same time as the acquisition of ASAR data. LAI estimated from the model was compared with ground measurements to evaluate the accuracy of the model. The results showed that the model provides a promising alternative to optical remote sensing data for predicting LAI of rice in southern China.

Taking place and development of desertification in the arid and semiarid regions directly influence the density and growth status of vegetation, making surface vegetation a most important indicator for desertification assessment. The primary purpose of this study was to assess the condition of desertification in central Asia and western China located in arid and semiarid regions. Remote sensing data used in this study were a time-series of 10-day maximum Normalized Difference Vegetation Index (NDVI) composites derived from Global Area Coverage of Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2000. The coefficient of variation (CoV) of the monthly NDVI (maximum-value composite) was used as a parameter to characterize the changes of vegetation in this work. The CoV can be used to compare the amount of variation in different sets of sample data. Changes in the value of the pixel-level CoV over time can be interpreted as a measure of vegetative biomass change over that time. The method to detect and quantify changes in CoV values for each pixel over a 20-year period for which data were available is based on linear regression. If the CoV values exhibit a statistically significant decrease over time, it is possible to conclude that the area imaged in that pixel is under desertification. The result was validated by comparison of the theoretical results to land cover maps in different years. This experiment demonstrated the feasibility of applying the CoV regression methodology and long term NOAA-AVHRR NDVI time-series data for desertification monitoring in central Asia and western China.

This paper will investigate the effectiveness of the genetic algorithm evolved neural network classifier and its application on the land cover classification of multi-spectral remotely sensed imagery. First, the key issues of the algorithms and the procedures are described in detail. Second, SPOT XS imagery is employed to evaluate its accuracy. Traditional classification algorithms, such as maximum likelihood classifier, back propagation neural network classifier, are also incorporated for a comparison purpose. Based on an evaluation of the user's accuracy and kappa statistic of different classifiers, the superiority of applying the discussed genetic algorithm-based classifier for land cover classification using multi-spectral imagery data is established. Finally, some concluding remarks and suggestions are also presented.